OpenRaiser/Intern-Atlas huggingface dataset:
Conditional Markov Chain Search for the Simple Plant Location Problem improves upper bounds on twelve Körkel-Ghosh instances
Venue: arXiv cs.DS 2017
Year: 2017

We address a family of hard benchmark instances for the Simple Plant Location Problem (also known as the Uncapacitated Facility Location Problem). The recent attempt by Fischetti et al. to tackle the Körkel-Ghosh instances resulted in seven new optimal solutions and 22 improved upper bounds. We use automated generation of heuristics to obtain a new algorithm for the Simple Plant Location Problem. In our experiments, our new algorithm matched all the previous best known and optimal solutions, and further improved 12 upper bounds, all within shorter time budgets compared to the previous efforts. Our algorithm design process is split into two phases: (i) development of algorithmic components such as local search procedures and mutation operators, and (ii) composition of a metaheuristic from the available components. Phase (i) requires human expertise and often can be completed by implementing several simple domain-specific routines known from the literature. Phase (ii) is entirely automated by employing the Conditional Markov Chain Search (CMCS) framework. In CMCS, a metaheuristic is flexibly defined by a set of parameters, called configuration. Then the process of composition of a metaheuristic from the algorithmic components is reduced to an optimisation problem seeking the best performing CMCS configuration. We discuss the problem of comparing configurations, and propose a new efficient technique to select the best performing configuration from a large set. To employ this method, we restrict the original CMCS to a simple deterministic case that leaves us with a finite and manageable number of meaningful configurations.
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Venue: arXiv cs.CV 2017
Year: 2017

Recognising objects according to a pre-defined fixed set of class labels has been well studied in the Computer Vision. There are a great many practical applications where the subjects that may be of interest are not known beforehand, or so easily delineated, however. In many of these cases natural language dialog is a natural way to specify the subject of interest, and the task achieving this capability (a.k.a, Referring Expression Comprehension) has recently attracted attention. To this end we propose a unified framework, the ParalleL AttentioN (PLAN) network, to discover the object in an image that is being referred to in variable length natural expression descriptions, from short phrases query to long multi-round dialogs. The PLAN network has two attention mechanisms that relate parts of the expressions to both the global visual content and also directly to object candidates. Furthermore, the attention mechanisms are recurrent, making the referring process visualizable and explainable. The attended information from these dual sources are combined to reason about the referred object. These two attention mechanisms can be trained in parallel and we find the combined system outperforms the state-of-art on several benchmarked datasets with different length language input, such as RefCOCO, RefCOCO+ and GuessWhat?!.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Venue: arXiv cs.CV 2017
Year: 2017

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.
Evolution of Social Power for Opinion Dynamics Networks
Venue: arXiv cs.SI 2017
Year: 2017

This article studies the evolution of opinions and interpersonal influence structures in a group of agents as they discuss a sequence of issues, each of which follows an opinion dynamics model. In this work, we propose a general opinion dynamics model and an evolution of interpersonal influence structures based on the model of reflected appraisals proposed by Friedkin. Our contributions can be summarized as follows: (i) we introduce a model of opinion dynamics and evolution of interpersonal influence structures between issues viewed as a best response cost minimization to the neighbor's actions, (ii) we show that DeGroot's and Friedkin-Johnsen's models of opinion dynamics and their evolution of interpersonal influence structures are particular cases of our proposed model, and (iii) we prove the existence of an equilibrium. This work is a step towards providing a solid formulation of the evolution of opinions and interpersonal influence structures over a sequence of issues.
Fundamentals of the Extremely Green, Flexible, and Profitable 5G M2M Ubiquitous Communications for Remote e-Healthcare and other Social e-Applications
Venue: arXiv cs.NI 2017
Year: 2017

The revolutionary trend of the up-to-date medicine can be formulated as wide introduction into basic medicine fields of electronic (e-health) and mobile (m-health) healthcare services and information applications. Unfortunately, all list of qualified m/e-healthcare services can be provided cost-effectively only in urban areas very good covered by broadband 4G/5G wireless communications. Unacceptably high investments are required into deployment of the optic core infrastructure for ubiquitous wide covering of sparsely populated rural, remote, and difficult for access (RRD) areas using the recent (4G) and forthcoming (5G) broadband radio access (RAN) centralized techniques, characterized by short cells ranges, because their profitability boundary exceeds several hundred residents per square km. Furthermore, the unprecedented requirements and new features of the forthcoming Internet of Things (IoT), machine-to-machine (M2M), and many other machine type IT-systems lead to a breakthrough in designing extremely green, flexible, and cost-effective technologies for future 5G wireless systems which will be able to reach in real time the performance extremums, trade-off optimums and fundamental limits. This paper examines the 5G PHY-MAC fundamentals and extremely approaches to creation of the profitable ubiquitous remote e/m-health services and telemedicine as the main innovation technology of popular healthcare and other social e-Applications for RRD territories. Proposed approaches lean on summarizing and develop the results of our previous works on RRD-adapted profitable ubiquitous green 4G/5G wireless multifunctional technologies.
Deep Local Binary Patterns
Venue: arXiv cs.CV 2017
Year: 2017

Local Binary Pattern (LBP) is a traditional descriptor for texture analysis that gained attention in the last decade. Being robust to several properties such as invariance to illumination translation and scaling, LBPs achieved state-of-the-art results in several applications. However, LBPs are not able to capture high-level features from the image, merely encoding features with low abstraction levels. In this work, we propose Deep LBP, which borrow ideas from the deep learning community to improve LBP expressiveness. By using parametrized data-driven LBP, we enable successive applications of the LBP operators with increasing abstraction levels. We validate the relevance of the proposed idea in several datasets from a wide range of applications. Deep LBP improved the performance of traditional and multiscale LBP in all cases.
Evaluating Roles of Central Users in Online Communication Networks: A Case Study of #PanamaLeaks
Venue: arXiv cs.SI 2017
Year: 2017

Social media has changed the ways of communication, where everyone is equipped with the power to express their opinions to others in online discussion platforms. Previously, a number of stud- ies have been presented to identify opinion leaders in online discussion networks. Feng ("Are you connected? Evaluating information cascade in online discussion about the #RaceTogether campaign", Computers in Human Behavior, 2016) identified five types of central users and their communication patterns in an online communication network of a limited time span. However, to trace the change in communication pattern, a long-term analysis is required. In this study, we critically analyzed framework presented by Feng based on five types of central users in online communication network and their communication pattern in a long-term manner. We take another case study presented by Udnor et al. ("Determining social media impact on the politics of developing countries using social network analytics", Program, 2016) to further understand the dynamics as well as to perform validation . Results indicate that there may not exist all of these central users in an online communication network in a long-term manner. Furthermore, we discuss the changing positions of opinion leaders and their power to keep isolates interested in an online discussion network.
Style Transfer in Text: Exploration and Evaluation
Venue: arXiv cs.CL 2017
Year: 2017

Style transfer is an important problem in natural language processing (NLP). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics. In this paper, we propose to learn style transfer with non-parallel data. We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation. We access our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
Multi-vehicle Path Following using Modified Trajectory Shaping Guidance
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we formulate a virtual target-based path following guidance law aimed towards multi-vehicle path following problem. The guidance law is well suited to precisely follow circular paths while minting desired distance between two adjacent vehicles where path information is only available to the lead vehicle. We analytically show lateral and longitudnal stability and convergence on the path. This is also validated through simulation and experimental results.
Bio-Inspired Local Information-Based Control for Probabilistic Swarm Distribution Guidance
Venue: arXiv cs.MA 2017
Year: 2017

This paper addresses a task allocation problem for a large-scale robotic swarm, namely swarm distribution guidance problem. Unlike most of the existing frameworks handling this problem, the proposed framework suggests utilising local information available to generate its time-varying stochastic policies. As each agent requires only local consistency on information with neighbouring agents, rather than the global consistency, the proposed framework offers various advantages, e.g., a shorter timescale for using new information and potential to incorporate an asynchronous decision-making process. We perform theoretical analysis on the properties of the proposed framework. From the analysis, it is proved that the framework can guarantee the convergence to the desired density distribution even using local information while maintaining advantages of global-information-based approaches. The design requirements for these advantages are explicitly listed in this paper. This paper also provides specific examples of how to implement the framework developed. The results of numerical experiments confirm the effectiveness and comparability of the proposed framework, compared with the global-information-based framework.
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
Venue: arXiv cs.CL 2017
Year: 2017

Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research.
Automatic link extraction: The good, the bad and the ugly in software ecosystem mining
Venue: arXiv cs.SE 2017
Year: 2017

This abstract presents the automatic link extraction pitfalls based on our experience on manually investigating links in the RubyGems package manager metadata. This work can lead in automating the link extraction approach so as to avoid these pitfalls and produce more complete datasets to be used by researchers when they investigate the multi-platform evolution of software ecosystems.
CPG-Based Control Scheme for Quadruped Robot to Withstand the Lateral Impact
Venue: arXiv cs.RO 2017
Year: 2017

This paper aims to present a stability control strategy for quadruped robot under lateral impact with the help of lateral trot. We firstly propose five necessary conditions for keeping balance. The classical four-neuron Central Pattern Generator (CPG) network with Hopf oscillators is then extended to eight-neuron network with four more trigger-enabled neurons, which controls the lateral trot. With proper adjustment of network's parameters, such network can coordinate the lateral and longitudinal trot gait. Based on Zero Movement Point (ZMP) theory, the robot is modeled as an inverted pendulum to plan the Center of Gravity (CoG) position and calculate the needed lateral step length. The simulation shows that the lateral acceleration of the quadruped robot after lateral impact regains to the normal range in a short time. Comparison shows that the maximal lateral impact that robot can resist increases about 125% from 0.72g to 1.55g.
Analysis of RLD Circuit by means of Phase Space with a non-ideal diode model
Venue: arXiv cs.SY 2017
Year: 2017

In this work the non-linear and chaotic behaviour for the RLD circuit is analysed, which is modelled by state space and numerically simulated considering the diode model an association between nonlinear resistances and capacitances. It has been possible to characterize the dynamics of the circuit through period doubles and chaotic behaviour. The Lyapunov exponent is calculated to show the chaotic behaviour of this circuit. Simulated results are in good agreement with experimental data.
Prior-aware Dual Decomposition: Document-specific Topic Inference for Spectral Topic Models
Venue: arXiv cs.CL 2017
Year: 2017

Spectral topic modeling algorithms operate on matrices/tensors of word co-occurrence statistics to learn topic-specific word distributions. This approach removes the dependence on the original documents and produces substantial gains in efficiency and provable topic inference, but at a cost: the model can no longer provide information about the topic composition of individual documents. Recently Thresholded Linear Inverse (TLI) is proposed to map the observed words of each document back to its topic composition. However, its linear characteristics limit the inference quality without considering the important prior information over topics. In this paper, we evaluate Simple Probabilistic Inverse (SPI) method and novel Prior-aware Dual Decomposition (PADD) that is capable of learning document-specific topic compositions in parallel. Experiments show that PADD successfully leverages topic correlations as a prior, notably outperforming TLI and learning quality topic compositions comparable to Gibbs sampling on various data.
On the Stability of a N-class Aloha Network
Venue: arXiv cs.IT 2017
Year: 2017

Necessary and sufficient conditions are established for the stability of a high-mobility N-class Aloha network, where the position of the sources follows a Poisson point process, each source has an infinity capacity buffer, packets arrive according to a Bernoulli distribution and the link distance between source and destination follows a Rayleigh distribution. It is also derived simple formulas for the stationary packet success probability and mean delay.
Schlegel Diagram and Optimizable Immediate Snapshot Protocol
Venue: arXiv cs.DC 2017
Year: 2017

In the topological study of distributed systems, the immediate snapshot is the fundamental computation block for the topological characterization of wait-free solvable tasks. However, in reality, the immediate snapshot is not available as a native built-in operation on shared memory distributed systems. Borowsky and Gafni have proposed a wait-free multi-round protocol that implements the immediate snapshot using more primitive operations, namely the atomic reads and writes. In this paper, up to an appropriate reformulation on the original protocol by Borowsky and Gafni, we establish a tight link between each round of the protocol and a topological operation of subdivision using Schlegel diagram. Due to the fact shown by Kozlov that the standard chromatic subdivision is obtained by iterated subdivision using Schlegel diagram, the reformulated version is proven to compute the immediate snapshot in a topologically smoother way. We also show that the reformulated protocol is amenable to optimization: Since each round restricts the possible candidates of output to an iteratively smaller region of finer subdivision, each process executing the protocol can decide at an earlier round, beyond which the same final output is reached no matter how the remaining rounds are executed. This reduces the number of read and write operations involved in the overall execution of the protocol, relieving the bottleneck of access to shared memory.
Optimal binary linear locally repairable codes with disjoint repair groups
Venue: arXiv cs.IT 2017
Year: 2017

In recent years, several classes of codes are introduced to provide some fault-tolerance and guarantee system reliability in distributed storage systems, among which locally repairable codes (LRCs for short) play an important role. However, most known constructions are over large fields with sizes close to the code length, which lead to the systems computationally expensive. Due to this, binary LRCs are of interest in practice. In this paper, we focus on binary linear LRCs with disjoint repair groups. We first derive an explicit bound for the dimension k of such codes, which can be served as a generalization of the bounds given in [11, 36, 37]. We also give several new constructions of binary LRCs with minimum distance $d = 6$ based on weakly independent sets and partial spreads, which are optimal with respect to our newly obtained bound. In particular, for locality $r\in \{2,3\}$ and minimum distance $d = 6$, we obtain the desired optimal binary linear LRCs with disjoint repair groups for almost all parameters.
Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intra-class features are gathered while inter-class variations are enlarged. Based on the learned architecture, the extracted spectrum-based features are classified by a center classifier. Moreover, to fuse the spectral and spatial information, an adaptive spectral-spatial center classifier is developed, where multiscale neighborhoods are considered simultaneously, and the final label is determined using an adaptive voting strategy. Finally, experimental results on three well-known datasets validate the effectiveness of the proposed methods compared with the state-of-the-art approaches.
Interactive, Intelligent Tutoring for Auxiliary Constructions in Geometry Proofs
Venue: arXiv cs.HC 2017
Year: 2017

Geometry theorem proving forms a major and challenging component in the K-12 mathematics curriculum. A particular difficult task is to add auxiliary constructions (i.e, additional lines or points) to aid proof discovery. Although there exist many intelligent tutoring systems proposed for geometry proofs, few teach students how to find auxiliary constructions. And the few exceptions are all limited by their underlying reasoning processes for supporting auxiliary constructions. This paper tackles these weaknesses of prior systems by introducing an interactive geometry tutor, the Advanced Geometry Proof Tutor (AGPT). It leverages a recent automated geometry prover to provide combined benefits that any geometry theorem prover or intelligent tutoring system alone cannot accomplish. In particular, AGPT not only can automatically process images of geometry problems directly, but also can interactively train and guide students toward discovering auxiliary constructions on their own. We have evaluated AGPT via a pilot study with 78 high school students. The study results show that, on training students how to find auxiliary constructions, there is no significant perceived difference between AGPT and human tutors, and AGPT is significantly more effective than the state-of-the-art geometry solver that produces human-readable proofs.